README.md

---
language: en
license: apache-2.0
tags:
  - pytorch
  - text-classification
  - dei
  - bias-detection
  - equibert
datasets:
  - synthetic
metrics:
  - f1
  - roc_auc
---

# EquiBERT — Bias Classifier

**Model ID:** `SallySims/equibert-bias-classifier`

Multi-label classifier that detects seven types of bias in
organisational text — job descriptions, HR communications,
policies, and workplace language.

## Labels

| ID | Label | Description |
|----|-------|-------------|
| 0 | `gender_bias` | Gendered language, role assumptions, masculine-coded words |
| 1 | `racial_bias` | Racial coding, cultural fit language, tokenism |
| 2 | `age_bias` | Digital native language, overqualified framing, generational stereotypes |
| 3 | `ability_bias` | Ableist language, physical requirements, disability framing |
| 4 | `socioeconomic_bias` | Class-coded language, credential gatekeeping |
| 5 | `cultural_bias` | Cultural exclusion, religious insensitivity |
| 6 | `intersectional` | Compounding bias across multiple identity dimensions |

## Usage

```python
from transformers import AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("SallySims/equibert-bias-classifier")

text = "We need a rock star developer who can dominate the roadmap."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# Load model weights and run inference
# (use with the EquiBERT modeling code from the repository)
```

## Task Head Architecture

```
CLS token → Dropout(0.1) → Linear(hidden, hidden//2) → GELU → Linear(hidden//2, 7)
                                                                       ↓
                                                          BCEWithLogitsLoss (multi-label)
                                                          Sigmoid threshold @ 0.5
```

## Performance (synthetic data, seed=42)

| Metric | Score |
|--------|-------|
| Macro F1 | 0.72 |
| Micro F1 | 0.76 |
| AUC | 0.81 |

## Model Description

EquiBERT is a multi-task DEI (Diversity, Equity and Inclusion) transformer
built on a dual-encoder backbone that fuses **RoBERTa-base** and
**DeBERTa-v3-base** via a learned weighted sum (α parameter).
The fused representation is fed into task-specific heads covering
17 distinct DEI analysis tasks.

**Organisation:** [SallySims](https://huggingface.co/SallySims)
**Framework:** PyTorch + HuggingFace Transformers
**Backbone:** RoBERTa-base + DeBERTa-v3-base (dual encoder, fused)
**Language:** English
**Domain:** Organisational DEI text — HR communications, policies,
job descriptions, performance reviews, leadership statements, reports

## Architecture

```
Input Text
    │
    ├──▶ RoBERTa-base encoder ──▶ Linear projection
    │                                     │
    └──▶ DeBERTa-v3-base encoder ──▶ Linear projection
                                          │
                              Weighted fusion (learned α)
                                          │
                                   Layer Norm + Dropout
                                          │
                              Task-specific head (see below)
```

## Training Data

Trained on synthetic DEI organisational text generated by the
EquiBERT synthetic data pipeline, covering 20 DEI categories
across HR, policy, leadership, and workforce analytics domains.
For production use, fine-tune on real labelled DEI data.

## Limitations

- Trained on synthetic data — predictions should be validated
  before use in real HR or policy decisions.
- English-only.
- Not a substitute for qualified DEI practitioners or legal advice.
- May reflect biases present in the training corpus.

## Citation

If you use EquiBERT in your research, please cite:

```bibtex
@misc{equibert2024,
  author    = {SallySims},
  title     = {EquiBERT: A Multi-Task DEI Transformer},
  year      = {2024},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/SallySims}
}
```

## All EquiBERT Models

| Model | Task | Primary Metric |
|-------|------|---------------|
| [equibert-bias-classifier](https://huggingface.co/SallySims/equibert-bias-classifier) | Bias Detection | Macro F1 |
| [equibert-microaggression](https://huggingface.co/SallySims/equibert-microaggression) | Microaggression Detection | Macro F1 |
| [equibert-category-tagger](https://huggingface.co/SallySims/equibert-category-tagger) | DEI Category Tagging | Macro F1 |
| [equibert-event-exclusion](https://huggingface.co/SallySims/equibert-event-exclusion) | Event Exclusion Classification | Macro F1 |
| [equibert-inclusive-language](https://huggingface.co/SallySims/equibert-inclusive-language) | Inclusive Language Scoring | Span F1 |
| [equibert-review-auditor](https://huggingface.co/SallySims/equibert-review-auditor) | Performance Review Auditing | Span F1 |
| [equibert-washing-detector](https://huggingface.co/SallySims/equibert-washing-detector) | DEI Washing Detection | MAE |
| [equibert-framing-scorer](https://huggingface.co/SallySims/equibert-framing-scorer) | Report Framing Scoring | MAE |
| [equibert-awareness-scorer](https://huggingface.co/SallySims/equibert-awareness-scorer) | DEI Awareness Scoring | MAE |
| [equibert-similarity](https://huggingface.co/SallySims/equibert-similarity) | Semantic Similarity | Accuracy |
| [equibert-ner](https://huggingface.co/SallySims/equibert-ner) | DEI Entity Recognition | Span F1 |
| [equibert-relation-extraction](https://huggingface.co/SallySims/equibert-relation-extraction) | Relation Extraction | Macro F1 |
| [equibert-qa](https://huggingface.co/SallySims/equibert-qa) | Extractive QA | Span EM |
| [equibert-search](https://huggingface.co/SallySims/equibert-search) | Semantic Search | MRR@10 |
| [equibert-nli](https://huggingface.co/SallySims/equibert-nli) | NLI / Textual Entailment | Macro F1 |
| [equibert-generator](https://huggingface.co/SallySims/equibert-generator) | DEI Text Generation | ROUGE-L |